As part of implementing the many products and services offered by Google, we have built a collection of systems and tools that simplify the storing and processing of large-scale data sets, and the construction of heavily-used public services based on these data sets. These systems are intended to work well in Google's computational environment, which consists of large numbers of commodity machines connected by commodity networking hardware. Our systems handle issues like storage reliability and availability in the face of machine failures, and our processing tools make it relatively easy to write robust computations that run reliably and efficiently on thousands of machines. In this talk I'll highlight some of the systems we have built, and discuss some challenges and future directions for new systems.
A two-and-a-half year study of DRAM on 10s of thousands Google servers found DIMM error rates are hundreds to thousands of times higher than thought — a mean of 3,751 correctable errors per DIMM per year. This is the world’s first large-scale study of RAM errors in the field. It looked at multiple vendors, DRAM densities and DRAM types including DDR1, DDR2 and FB-DIMM.
"I found the Google translate is pretty cool. I would like to use it to translate some text from my computer in batch(in script). Do you know any way that I could use it easily from shell?" And here we go...
Search millions of photographs from the LIFE photo archive, stretching from the 1750s to today. Most were never published and are now available for the first time through the joint work of LIFE and Google.
